code: def dummy_unet(self):
      torch.manual_seed(0)
  
      model_kwargs = {
          "in_channels": 8,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image_hint",
          "down_block_types": (
              "ResnetDownsampleBlock2D",
              "SimpleCrossAttnDownBlock2D",
          ),
          "up_block_types": (
              "SimpleCrossAttnUpBlock2D",
              "ResnetUpsampleBlock2D",
          ),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (
              self.block_out_channels_0,
              self.block_out_channels_0 * 2,
          ),
          "layers_per_block": 1,
          "encoder_hid_dim": self.text_embedder_hidden_size,
          "encoder_hid_dim_type": "image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }
  
      model = UNet2DConditionModel(**model_kwargs)
      return model
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from diffusers import DDIMScheduler, KandinskyV22ControlnetImg2ImgPipeline, KandinskyV22PriorEmb2EmbPipeline, UNet2DConditionModel, VQModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, nightly, require_torch_gpu, torch_device
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 6 for calling the KandinskyV22ControlnetImg2ImgPipelineFastTests obj's underlying member method code with example usage: obj.dummy_unet() and returns: model